code: def make_covar_matrix(
      covariance_type, n_components, n_features, random_state=None
  ):
      mincv = 0.1
      prng = check_random_state(random_state)
      if covariance_type == "spherical":
          return (
              mincv + mincv * prng.random_sample((n_components,))
          ) ** 2
      elif covariance_type == "tied":
          return make_spd_matrix(n_features) + mincv * np.eye(
              n_features
          )
      elif covariance_type == "diag":
          return (
              mincv
              + mincv * prng.random_sample((n_components, n_features))
          ) ** 2
      elif covariance_type == "full":
          return np.array(
              [
                  (
                      make_spd_matrix(n_features, random_state=prng)
                      + mincv * np.eye(n_features)
                  )
                  for x in range(n_components)
              ]
          )
  
file_path: hmmlearn/lib/hmmlearn/tests/__init__.py
imports: import numpy
  import pytest
  from sklearn.datasets import make_spd_matrix
  from sklearn.utils import check_random_state
  from hmmlearn.utils import normalize
  from hmmlearn.base import DECODER_ALGORITHMS
label_desc: Use image node_id 1 for calling a global function with example usage: make_covar_matrix(covariance_type, n_components, n_features, random_state) and returns: unknown, unknown, unknown, np